Algorithm Selection for Optimal Multi-Agent Path Finding via Graph Embedding

Carmel Shabalin, Omri Kaduri, Roni Stern·June 16, 2024

Summary

The paper presents a novel approach called Multi-Agent Path Finding Algorithm Selection via Graph embedding (MAG) for solving Multi-Agent Path Finding (MAPF) problems. MAG employs graph embeddings, specifically using FEATHER, to encode problem instances and combines this with handcrafted features and existing algorithms. It outperforms existing techniques in algorithm selection tasks, particularly in challenging setups like in-grid-type and between-grid, by improving accuracy and reducing regret compared to baselines like KBS and G2V. The study highlights the potential of graph embeddings in enhancing algorithm selection for MAPF, with future work focusing on further enhancing the model and integrating it with other approaches. The research contributes to the field by addressing the complexity of MAPF and improving the efficiency of solving it for real-world applications.

Key findings

4

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper addresses the problem of Algorithm Selection (AS) for Optimal Multi-Agent Path Finding (MAPF) . This problem involves determining the most suitable optimal MAPF algorithm to use for a given MAPF instance from a set of available algorithms . While the problem of AS for MAPF is not new, the paper introduces a novel approach called MAPF Algorithm selection via Graph embedding (MAG) that leverages graph embedding algorithms to enhance the selection process .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis that the proposed approach, MAG, which is based on graph embedding for optimal Multi-Agent Path Finding (MAPF) algorithm selection, outperforms existing baselines in terms of accuracy, coverage, runtime, and regret across different algorithm selection (AS) setups . The study aims to demonstrate that the combination of graph-embedding features and hand-crafted features in MAG leads to strong state-of-the-art performance for optimal MAPF . The experiments conducted in the paper show that MAG consistently outperforms existing baselines in various AS setups, such as In-Grid, In-Grid-Type, and Between-Grid, with significant advantages in terms of average regret, highlighting the effectiveness of the proposed approach .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Algorithm Selection for Optimal Multi-Agent Path Finding via Graph Embedding" proposes several innovative ideas, methods, and models in the field of multi-agent path finding (MAPF) based on graph embedding . Here are some key contributions outlined in the paper:

  1. MAG Approach: The paper introduces the MAG approach, which stands for Multi-Agent path finding Algorithm selection based on Graph embedding. MAG utilizes two encodings of the MAPF problem as a graph, one encoding the entire graph and the other encoding only the shortest paths and their immediate vicinity. This approach efficiently works on new MAPF problems without requiring prior training. By combining graph-embedding features with hand-crafted MAPF-specific features, MAG achieves state-of-the-art performance in optimal MAPF algorithm selection .

  2. Graph Embedding Algorithms: The paper discusses the significance of graph embedding algorithms in encoding nodes and entire graphs into low-dimensional continuous vectors. It highlights the usefulness of graph embeddings as features for various machine learning tasks such as classification. Notable graph embedding algorithms like Graph2Vec and FEATHER are mentioned, with FEATHER being a recently proposed algorithm that serves as both a node and graph embedding method, showing effectiveness in node-level and graph-level machine learning tasks .

  3. Automated Algorithm Selection: The study addresses the need for an automated way to select optimal MAPF solvers for specific problems. It emphasizes that different optimal algorithms are suitable for different problems and proposes an automated selection process based on graph embedding features and hand-crafted features. The paper demonstrates that MAG outperforms existing baselines in most scenarios, especially in challenging AS setups like In-Grid-Type and Between-Grid, showcasing a significant advantage in terms of average regret .

  4. Experimental Validation: The paper provides experimental validation of the proposed methods by comparing MAG against baseline methods like KBS and G2V. Statistical significance tests, such as one-sided t tests, are conducted to verify the advantage of MAG over the baselines in terms of accuracy, coverage, and regret. The results show that MAG significantly outperforms the baseline methods, highlighting the effectiveness of the proposed approach in optimal MAPF algorithm selection . The proposed approach, MAG (Multi-Agent path finding Algorithm selection based on Graph embedding), offers several key characteristics and advantages compared to previous methods in the field of optimal Multi-Agent Path Finding (MAPF) algorithm selection .

  5. Graph Embedding Utilization: MAG utilizes a modern graph embedding algorithm, FEATHER, to encode the entire graph and the shortest paths of MAPF problems. This approach does not require prior training and can be applied on-the-fly to any given graph, providing flexibility and efficiency in handling new MAPF instances .

  6. Combination of Features: MAG combines graph-embedding features with hand-crafted MAPF-specific features, resulting in a comprehensive set of features for optimal algorithm selection. By integrating multiple encodings seamlessly, MAG offers a more thorough encoding of the problem, enhancing the performance of the algorithm selection process .

  7. Performance Improvement: In extensive experiments on a standard benchmark, MAG consistently outperformed existing baselines in terms of accuracy, coverage, and regret. The results demonstrate that MAG achieves strong state-of-the-art performance in optimal MAPF algorithm selection, especially in challenging AS setups like In-Grid-Type and Between-Grid, showcasing a significant advantage in terms of average regret .

  8. Automated Selection Process: MAG introduces a practical and automated approach to optimal MAPF algorithm selection based on graph embedding. This method addresses the need for an efficient way to select the most suitable MAPF solvers for specific problems, offering superior performance compared to traditional supervised learning paradigms .

  9. Flexibility and Generalization: MAG's encoding method, FG2V, fully utilizes the power of graph embedding algorithms and allows for encoding any given graph, augmented with artificial edges. This approach enhances the generalization capability of the algorithm, making it applicable to a wide range of MAPF instances without the need for re-training .

In summary, MAG stands out for its innovative use of graph embedding, the integration of diverse features, superior performance in algorithm selection, and the ability to handle new MAPF problems efficiently and effectively, making it a significant advancement in the field of optimal Multi-Agent Path Finding algorithm selection .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies have been conducted in the field of Multi-Agent Path Finding (MAPF). Noteworthy researchers in this field include H. Ma, J. Yang, L. Cohen, T. Kumar, S. Koenig, P. Surynek, J. Yu, S. M. LaValle, Daniel Kornhauser, Gary Miller, J. Li, D. Harabor, A. Felner, R. Stern, G. Sharon, N. R. Sturtevant, E. Lam, P. Le Bodic, E. Boyarski, among others . These researchers have contributed to various aspects of MAPF, including algorithm selection, path planning for multiple robots, and optimal solutions for MAPF problems.

The key to the solution mentioned in the paper "Algorithm Selection for Optimal Multi-Agent Path Finding via Graph Embedding" is the development of an approach called MAG (Multi-Agent Path Finding Algorithm Selection) . MAG utilizes graph embedding techniques to encode MAPF instances efficiently. It combines two encodings of the MAPF problem as a graph, one encoding the entire graph and the other encoding only the shortest paths and their immediate vicinity. By leveraging modern graph embedding algorithms like FEATHER, MAG achieves superior results in selecting optimal MAPF algorithms for given instances. The combination of graph-embedding features and hand-crafted MAPF-specific features leads to strong state-of-the-art algorithm selection for optimal MAPF .


How were the experiments in the paper designed?

The experiments in the paper were designed with three distinct setups based on Algorithm Selection (AS) problems for Multi-Agent Path Finding (MAPF) as defined by Kaduri et al. . These setups include:

  1. In-grid AS: where the train and test problems are all from the same underlying graph .
  2. In-grid-type AS: where the train and test problems are on different grids, but their grids have similar topologies .
  3. Between-grid-type AS: where the graphs in the train and test problems are completely different, such as training on maze-like graphs and testing on graphs representing roadmaps in a city .

The paper utilized these setups to evaluate the performance of the Multi-Agent Graph (MAG) algorithm against baselines like KBS and G2V in various metrics such as accuracy, coverage, runtime, and regret . The experiments aimed to showcase the effectiveness of MAG over the baselines in different AS problem scenarios, highlighting the challenges and advantages of each setup .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is a publicly available grid-based MAPF benchmark containing 33 grids arranged into seven grid types, such as video games, city maps, maze-like grids, rooms, empty grids, grids with randomly placed obstacles, and grids inspired by warehouse structures . The authors are fully committed to making the code, datasets, and results publicly available once the paper is accepted .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed to be verified. The study conducted a comprehensive evaluation of the Algorithm Selection (AS) for Optimal Multi-Agent Path Finding (MAPF) using Graph Embedding . The experiments involved three different AS setups: In-Grid, In-Grid-Type, and Between-Grid-Type, as defined by Kaduri et al. . These setups allowed for a thorough analysis of the performance of the proposed MAG approach in various scenarios.

In the experiments, MAG consistently outperformed the baseline methods, KBS and G2V, in terms of accuracy, coverage, runtime, and regret across different AS setups . For example, in the In-Grid-Type setup, MAG achieved an accuracy of 0.71, coverage of 0.94, and runtime of 0.80, with a regret of 67.9, showcasing its superiority over the baselines . The results demonstrated that MAG provided significant advantages over the existing methods in most metrics, highlighting its effectiveness in optimal MAPF algorithm selection.

Furthermore, the study conducted ablation studies to analyze the impact of combining hand-crafted features with graph embedding features . The results indicated that combining features from KBS with graph embedding led to the most substantial performance improvement, emphasizing the importance of feature combination in achieving optimal AS for MAPF .

Overall, the experimental results presented in the paper not only validated the scientific hypotheses but also showcased the effectiveness of the MAG approach in achieving state-of-the-art AS for optimal MAPF. The thorough evaluation across different AS setups and the comparison with baseline methods provided robust evidence supporting the superiority of MAG in terms of accuracy, coverage, runtime, and regret .


What are the contributions of this paper?

The paper makes several contributions in the field of multi-agent path finding via graph embedding:

  • It provides a feasibility study on moving non-homogeneous teams in congested video game environments .
  • It explores an optimization variant of multi-robot path planning, highlighting its intractability .
  • The paper delves into the structure and intractability of optimal multi-robot path planning on graphs .
  • It discusses coordinating pebble motion on graphs, the diameter of permutation groups, and their applications .
  • The research introduces a novel approach to path planning for multiple robots in bi-connected graphs .
  • It presents pairwise symmetry reasoning for multi-agent path finding search .
  • The paper discusses enhanced partial expansion A* .
  • It explores branch-and-cut-and-price for multi-agent path finding .
  • The study generalizes multi-agent path finding for heterogeneous agents .
  • It addresses multi-agent pathfinding with continuous time .
  • The research focuses on multi-agent path finding for large agents .
  • It discusses conflict-based search for optimal multi-agent pathfinding .
  • The paper explores automatic algorithm selection in multi-agent pathfinding .

What work can be continued in depth?

One area for further exploration highlighted in the study is the combination of graph-embedding based Algorithm Selection (AS) models with image-based AS models, such as MAP-FASTER [19]. This integration could enhance the performance and capabilities of AS models for optimal Multi-Agent Path Finding (MAPF) . Additionally, the study suggests that future work could focus on the challenging between-grid AS setup, which involves training on one type of grid and testing on a different type of grid, making the classification problem significantly harder . Further research in this direction could lead to advancements in addressing the complexities of diverse grid structures in MAPF scenarios.

Tables

3

Introduction
Background
Overview of Multi-Agent Path Finding (MAPF) problems
Challenges and complexity of existing solutions
Objective
To propose a novel algorithm selection method for MAPF
Improve accuracy and reduce regret in complex scenarios
Address real-world application efficiency
Method
Data Collection
Problem Instance Encoding
Use of graph embeddings (FEATHER) for problem representation
Encoding of problem characteristics and structure
Feature Engineering
Integration of handcrafted features
Importance of features for algorithm selection
Baselines
Comparison with KBS and G2V algorithm selection techniques
Evaluation of baseline performance
MAG Algorithm
Description of the algorithm's workflow
Integration of graph embeddings and handcrafted features
Performance Evaluation
Experimental setup and datasets
Accuracy and regret metrics
Results and Analysis
Comparison of MAG with baseline methods
Improvement in solving challenging scenarios (in-grid-type, between-grid)
Discussion of results and significance
Future Work
Potential enhancements to the model
Integration with other MAPF solving approaches
Directions for further research
Conclusion
Summary of the paper's contributions
Implications for the field of Multi-Agent Path Finding
Real-world application prospects
Basic info
papers
artificial intelligence
Advanced features
Insights
What is the primary focus of the paper described?
What are the key components of the MAG method, as mentioned in the text?
How does MAG compare to KBS and G2V in terms of accuracy and regret in solving MAPF problems?
How does the Multi-Agent Path Finding Algorithm Selection via Graph embedding (MAG) approach address the challenge of MAPF?

Algorithm Selection for Optimal Multi-Agent Path Finding via Graph Embedding

Carmel Shabalin, Omri Kaduri, Roni Stern·June 16, 2024

Summary

The paper presents a novel approach called Multi-Agent Path Finding Algorithm Selection via Graph embedding (MAG) for solving Multi-Agent Path Finding (MAPF) problems. MAG employs graph embeddings, specifically using FEATHER, to encode problem instances and combines this with handcrafted features and existing algorithms. It outperforms existing techniques in algorithm selection tasks, particularly in challenging setups like in-grid-type and between-grid, by improving accuracy and reducing regret compared to baselines like KBS and G2V. The study highlights the potential of graph embeddings in enhancing algorithm selection for MAPF, with future work focusing on further enhancing the model and integrating it with other approaches. The research contributes to the field by addressing the complexity of MAPF and improving the efficiency of solving it for real-world applications.
Mind map
Encoding of problem characteristics and structure
Use of graph embeddings (FEATHER) for problem representation
Accuracy and regret metrics
Experimental setup and datasets
Integration of graph embeddings and handcrafted features
Description of the algorithm's workflow
Evaluation of baseline performance
Comparison with KBS and G2V algorithm selection techniques
Importance of features for algorithm selection
Integration of handcrafted features
Problem Instance Encoding
Address real-world application efficiency
Improve accuracy and reduce regret in complex scenarios
To propose a novel algorithm selection method for MAPF
Challenges and complexity of existing solutions
Overview of Multi-Agent Path Finding (MAPF) problems
Real-world application prospects
Implications for the field of Multi-Agent Path Finding
Summary of the paper's contributions
Directions for further research
Integration with other MAPF solving approaches
Potential enhancements to the model
Discussion of results and significance
Improvement in solving challenging scenarios (in-grid-type, between-grid)
Comparison of MAG with baseline methods
Performance Evaluation
MAG Algorithm
Baselines
Feature Engineering
Data Collection
Objective
Background
Conclusion
Future Work
Results and Analysis
Method
Introduction
Outline
Introduction
Background
Overview of Multi-Agent Path Finding (MAPF) problems
Challenges and complexity of existing solutions
Objective
To propose a novel algorithm selection method for MAPF
Improve accuracy and reduce regret in complex scenarios
Address real-world application efficiency
Method
Data Collection
Problem Instance Encoding
Use of graph embeddings (FEATHER) for problem representation
Encoding of problem characteristics and structure
Feature Engineering
Integration of handcrafted features
Importance of features for algorithm selection
Baselines
Comparison with KBS and G2V algorithm selection techniques
Evaluation of baseline performance
MAG Algorithm
Description of the algorithm's workflow
Integration of graph embeddings and handcrafted features
Performance Evaluation
Experimental setup and datasets
Accuracy and regret metrics
Results and Analysis
Comparison of MAG with baseline methods
Improvement in solving challenging scenarios (in-grid-type, between-grid)
Discussion of results and significance
Future Work
Potential enhancements to the model
Integration with other MAPF solving approaches
Directions for further research
Conclusion
Summary of the paper's contributions
Implications for the field of Multi-Agent Path Finding
Real-world application prospects
Key findings
4

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper addresses the problem of Algorithm Selection (AS) for Optimal Multi-Agent Path Finding (MAPF) . This problem involves determining the most suitable optimal MAPF algorithm to use for a given MAPF instance from a set of available algorithms . While the problem of AS for MAPF is not new, the paper introduces a novel approach called MAPF Algorithm selection via Graph embedding (MAG) that leverages graph embedding algorithms to enhance the selection process .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis that the proposed approach, MAG, which is based on graph embedding for optimal Multi-Agent Path Finding (MAPF) algorithm selection, outperforms existing baselines in terms of accuracy, coverage, runtime, and regret across different algorithm selection (AS) setups . The study aims to demonstrate that the combination of graph-embedding features and hand-crafted features in MAG leads to strong state-of-the-art performance for optimal MAPF . The experiments conducted in the paper show that MAG consistently outperforms existing baselines in various AS setups, such as In-Grid, In-Grid-Type, and Between-Grid, with significant advantages in terms of average regret, highlighting the effectiveness of the proposed approach .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Algorithm Selection for Optimal Multi-Agent Path Finding via Graph Embedding" proposes several innovative ideas, methods, and models in the field of multi-agent path finding (MAPF) based on graph embedding . Here are some key contributions outlined in the paper:

  1. MAG Approach: The paper introduces the MAG approach, which stands for Multi-Agent path finding Algorithm selection based on Graph embedding. MAG utilizes two encodings of the MAPF problem as a graph, one encoding the entire graph and the other encoding only the shortest paths and their immediate vicinity. This approach efficiently works on new MAPF problems without requiring prior training. By combining graph-embedding features with hand-crafted MAPF-specific features, MAG achieves state-of-the-art performance in optimal MAPF algorithm selection .

  2. Graph Embedding Algorithms: The paper discusses the significance of graph embedding algorithms in encoding nodes and entire graphs into low-dimensional continuous vectors. It highlights the usefulness of graph embeddings as features for various machine learning tasks such as classification. Notable graph embedding algorithms like Graph2Vec and FEATHER are mentioned, with FEATHER being a recently proposed algorithm that serves as both a node and graph embedding method, showing effectiveness in node-level and graph-level machine learning tasks .

  3. Automated Algorithm Selection: The study addresses the need for an automated way to select optimal MAPF solvers for specific problems. It emphasizes that different optimal algorithms are suitable for different problems and proposes an automated selection process based on graph embedding features and hand-crafted features. The paper demonstrates that MAG outperforms existing baselines in most scenarios, especially in challenging AS setups like In-Grid-Type and Between-Grid, showcasing a significant advantage in terms of average regret .

  4. Experimental Validation: The paper provides experimental validation of the proposed methods by comparing MAG against baseline methods like KBS and G2V. Statistical significance tests, such as one-sided t tests, are conducted to verify the advantage of MAG over the baselines in terms of accuracy, coverage, and regret. The results show that MAG significantly outperforms the baseline methods, highlighting the effectiveness of the proposed approach in optimal MAPF algorithm selection . The proposed approach, MAG (Multi-Agent path finding Algorithm selection based on Graph embedding), offers several key characteristics and advantages compared to previous methods in the field of optimal Multi-Agent Path Finding (MAPF) algorithm selection .

  5. Graph Embedding Utilization: MAG utilizes a modern graph embedding algorithm, FEATHER, to encode the entire graph and the shortest paths of MAPF problems. This approach does not require prior training and can be applied on-the-fly to any given graph, providing flexibility and efficiency in handling new MAPF instances .

  6. Combination of Features: MAG combines graph-embedding features with hand-crafted MAPF-specific features, resulting in a comprehensive set of features for optimal algorithm selection. By integrating multiple encodings seamlessly, MAG offers a more thorough encoding of the problem, enhancing the performance of the algorithm selection process .

  7. Performance Improvement: In extensive experiments on a standard benchmark, MAG consistently outperformed existing baselines in terms of accuracy, coverage, and regret. The results demonstrate that MAG achieves strong state-of-the-art performance in optimal MAPF algorithm selection, especially in challenging AS setups like In-Grid-Type and Between-Grid, showcasing a significant advantage in terms of average regret .

  8. Automated Selection Process: MAG introduces a practical and automated approach to optimal MAPF algorithm selection based on graph embedding. This method addresses the need for an efficient way to select the most suitable MAPF solvers for specific problems, offering superior performance compared to traditional supervised learning paradigms .

  9. Flexibility and Generalization: MAG's encoding method, FG2V, fully utilizes the power of graph embedding algorithms and allows for encoding any given graph, augmented with artificial edges. This approach enhances the generalization capability of the algorithm, making it applicable to a wide range of MAPF instances without the need for re-training .

In summary, MAG stands out for its innovative use of graph embedding, the integration of diverse features, superior performance in algorithm selection, and the ability to handle new MAPF problems efficiently and effectively, making it a significant advancement in the field of optimal Multi-Agent Path Finding algorithm selection .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies have been conducted in the field of Multi-Agent Path Finding (MAPF). Noteworthy researchers in this field include H. Ma, J. Yang, L. Cohen, T. Kumar, S. Koenig, P. Surynek, J. Yu, S. M. LaValle, Daniel Kornhauser, Gary Miller, J. Li, D. Harabor, A. Felner, R. Stern, G. Sharon, N. R. Sturtevant, E. Lam, P. Le Bodic, E. Boyarski, among others . These researchers have contributed to various aspects of MAPF, including algorithm selection, path planning for multiple robots, and optimal solutions for MAPF problems.

The key to the solution mentioned in the paper "Algorithm Selection for Optimal Multi-Agent Path Finding via Graph Embedding" is the development of an approach called MAG (Multi-Agent Path Finding Algorithm Selection) . MAG utilizes graph embedding techniques to encode MAPF instances efficiently. It combines two encodings of the MAPF problem as a graph, one encoding the entire graph and the other encoding only the shortest paths and their immediate vicinity. By leveraging modern graph embedding algorithms like FEATHER, MAG achieves superior results in selecting optimal MAPF algorithms for given instances. The combination of graph-embedding features and hand-crafted MAPF-specific features leads to strong state-of-the-art algorithm selection for optimal MAPF .


How were the experiments in the paper designed?

The experiments in the paper were designed with three distinct setups based on Algorithm Selection (AS) problems for Multi-Agent Path Finding (MAPF) as defined by Kaduri et al. . These setups include:

  1. In-grid AS: where the train and test problems are all from the same underlying graph .
  2. In-grid-type AS: where the train and test problems are on different grids, but their grids have similar topologies .
  3. Between-grid-type AS: where the graphs in the train and test problems are completely different, such as training on maze-like graphs and testing on graphs representing roadmaps in a city .

The paper utilized these setups to evaluate the performance of the Multi-Agent Graph (MAG) algorithm against baselines like KBS and G2V in various metrics such as accuracy, coverage, runtime, and regret . The experiments aimed to showcase the effectiveness of MAG over the baselines in different AS problem scenarios, highlighting the challenges and advantages of each setup .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is a publicly available grid-based MAPF benchmark containing 33 grids arranged into seven grid types, such as video games, city maps, maze-like grids, rooms, empty grids, grids with randomly placed obstacles, and grids inspired by warehouse structures . The authors are fully committed to making the code, datasets, and results publicly available once the paper is accepted .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed to be verified. The study conducted a comprehensive evaluation of the Algorithm Selection (AS) for Optimal Multi-Agent Path Finding (MAPF) using Graph Embedding . The experiments involved three different AS setups: In-Grid, In-Grid-Type, and Between-Grid-Type, as defined by Kaduri et al. . These setups allowed for a thorough analysis of the performance of the proposed MAG approach in various scenarios.

In the experiments, MAG consistently outperformed the baseline methods, KBS and G2V, in terms of accuracy, coverage, runtime, and regret across different AS setups . For example, in the In-Grid-Type setup, MAG achieved an accuracy of 0.71, coverage of 0.94, and runtime of 0.80, with a regret of 67.9, showcasing its superiority over the baselines . The results demonstrated that MAG provided significant advantages over the existing methods in most metrics, highlighting its effectiveness in optimal MAPF algorithm selection.

Furthermore, the study conducted ablation studies to analyze the impact of combining hand-crafted features with graph embedding features . The results indicated that combining features from KBS with graph embedding led to the most substantial performance improvement, emphasizing the importance of feature combination in achieving optimal AS for MAPF .

Overall, the experimental results presented in the paper not only validated the scientific hypotheses but also showcased the effectiveness of the MAG approach in achieving state-of-the-art AS for optimal MAPF. The thorough evaluation across different AS setups and the comparison with baseline methods provided robust evidence supporting the superiority of MAG in terms of accuracy, coverage, runtime, and regret .


What are the contributions of this paper?

The paper makes several contributions in the field of multi-agent path finding via graph embedding:

  • It provides a feasibility study on moving non-homogeneous teams in congested video game environments .
  • It explores an optimization variant of multi-robot path planning, highlighting its intractability .
  • The paper delves into the structure and intractability of optimal multi-robot path planning on graphs .
  • It discusses coordinating pebble motion on graphs, the diameter of permutation groups, and their applications .
  • The research introduces a novel approach to path planning for multiple robots in bi-connected graphs .
  • It presents pairwise symmetry reasoning for multi-agent path finding search .
  • The paper discusses enhanced partial expansion A* .
  • It explores branch-and-cut-and-price for multi-agent path finding .
  • The study generalizes multi-agent path finding for heterogeneous agents .
  • It addresses multi-agent pathfinding with continuous time .
  • The research focuses on multi-agent path finding for large agents .
  • It discusses conflict-based search for optimal multi-agent pathfinding .
  • The paper explores automatic algorithm selection in multi-agent pathfinding .

What work can be continued in depth?

One area for further exploration highlighted in the study is the combination of graph-embedding based Algorithm Selection (AS) models with image-based AS models, such as MAP-FASTER [19]. This integration could enhance the performance and capabilities of AS models for optimal Multi-Agent Path Finding (MAPF) . Additionally, the study suggests that future work could focus on the challenging between-grid AS setup, which involves training on one type of grid and testing on a different type of grid, making the classification problem significantly harder . Further research in this direction could lead to advancements in addressing the complexities of diverse grid structures in MAPF scenarios.

Tables
3
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